Machine Learning-Based Prediction and Interpretation of Hot-Tearing Susceptibility in Al–Zn–Mg Alloys for Direct-Chill Casting Process Optimization

Hot tearing limits the yield of Al–Zn–Mg billets produced by direct-chill casting. Its governing factors span composition, cooling conditions and the state of the partially solidified metal, and they interact; several quantities required by physics-based criteria cannot be measured during casting, and the factors are seldom observed together at plant scale. Machine learning was therefore used to establish, from variables that are recorded in production, which factors govern hot tearing when composition and process conditions vary simultaneously. Hot tearing was assessed in 108 pilot-scale billets covering several variants, and models were trained on alloy composition, casting parameters and calculated solidification characteristics. Evaluation used nested cross-validation with whole composition clusters held out, so that performance reflects unseen variants; the selected model attained a pooled out-of-fold average precision of 0.63 against a no-skill level of 0.51. Shapley additive explanations identified Si, Mn, Zn and steady-state casting speed as the dominant variables; the effect of Si depended on Fe content and that of Mg was non-monotonic. The contribution of Mn has not been documented systematically in this system. Attribution of the model yielded relations consistent with previously reported experimental findings, and these relations rest on variables obtainable in production, providing a basis for optimizing alloy design and casting processes.